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Objective During object tracking, motion information can predict the location of the object. If motion information is ignored or motion is inaccurately modeled, then tracking may fail. To deal with this issue, we introduce visual saliency, which can quickly capture the interesting object, in tracking. Furthermore, we propose a tracking algorithm based on spatio-temporal motion saliency. Method First, we propose a bottom-up computational model for spatio-temporal motion saliency according to the hierarchical motion processing in the visual cortex. We adopt 3D spatio-temporal filters for the coding of underlying motion signals and max-pooling operation for the coding of local features. Considering the temporal relationship between the spatio-temporal motion features in the historical and current frames, we construct the spatio-temporal motion saliency map by measuring the difference between consecutive frames. Second, in the frame of particle filter, we measure the correlation between the predictive state and the observation by combining spatio-temporal motion saliency with color histogram. The object state can then be determined and tracked. Result Compared with other methods, our approach can stably track the objects under unfavorable situations, such as variable lighting, background clutter, motion blurs, occlusion, and deformation. We can improve the tracking performance in terms of central position error, precision, and success rate. In addition, we integrate the spatio-temporal motion saliency into other tracking methods and achieve better results, which demonstrates the effectiveness of the spatio-temporal motion saliency for object tracking. Conclusion The spatio-temporal motion saliency can improve tracking performance as it measures motion information effectively, thereby enhancing the salient area and suppressing interference. [ABSTRACT FROM AUTHOR]